Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information

Data-driven imitation learning enables service robots to learn social interaction behaviors, but these systems cannot adapt after training to changes in the environment, such as changing products in a store. To solve this, a novel learning system that uses neural attention and approximate string matching to copy information from a product information database to its output is proposed. A camera shop interaction dataset was simulated for training/testing. The proposed system was found to outperform a baseline and a previous state of the art in an offline, human-judged evaluation

Links between dissipation, intermittency, and helicity in the GOY model revisited

High-resolution simulations within the GOY shell model are used to study various scaling relations for turbulence. A power-law relation between the second-order intermittency correction and the crossover from the inertial to the dissipation range is confirmed. Evidence is found for the intermediate viscous dissipation range proposed by Frisch and Vergassola. It is emphasized that insufficient dissipation-range resolution systematically drives the energy spectrum towards statistical-mechanical equipartition. In fully resolved simulations the inertial-range scaling exponents depend on both model parameters; in particular, there is no evidence that the conservation of a helicity-like quantity leads to universal exponents.Comment: 24 pages, 13 figures; submitted to Physica

The role of organisms in hyporheic processes : gaps in current knowledge, needs for future research and applications

Fifty years after the hyporheic zone was first defined (Orghidan, 1959), there are still gaps in the knowledge regarding the role of biodiversity in hyporheic processes. First, some methodological questions remained unanswered regarding the interactions between biodiversity and physical processes, both for the study of habitat characteristics and interactions at different scales. Furthermore, many questions remain to be addressed to help inform our understanding of invertebrate community dynamics, especially regarding the trophic niches of organisms, the functional groups present within sediment, and their temporal changes. Understanding microbial community dynamics would require investigations about their relationship with the physical characteristics of the sediment, their diversity, their relationship with metabolic pathways, their inter- actions with invertebrates, and their response to environmental stress. Another fundamental research question is that of the importance of the hyporheic zone in the global metabolism of the river, which must be explored in relation to organic matter recycling, the effects of disturbances, and the degradation of contaminants. Finally, the application of this knowledge requires the development of methods for the estimation of hydro- logical exchanges, especially for the management of sediment clogging, the optimization of self-purification, and the integration of climate change in environmental policies. The development of descriptors of hyporheic zone health and of new metrology is also crucial to include specific targets in water policies for the long-term management of the system and a clear evaluation of restoration strategies

Collaborative Models for Referring Expression Generation in Situated Dialogue

In situated dialogue with artificial agents (e.g., robots), although a human and an agent are co-present, the agent's representation and the human's representation of the shared environment are significantly mismatched. Because of this misalignment, our previous work has shown that when the agent applies traditional approaches to generate referring expressions for describing target objects with minimum descriptions, the intended objects often cannot be correctly identified by the human. To address this problem, motivated by collaborative behaviors in human referential communication, we have developed two collaborative models - an episodic model and an installment model - for referring expression generation. Both models, instead of generating a single referring expression to describe a target object as in the previous work, generate multiple small expressions that lead to the target object with the goal of minimizing the collaborative effort. In particular, our installment model incorporates human feedback in a reinforcement learning framework to learn the optimal generation strategies. Our empirical results have shown that the episodic model and the installment model outperform previous non-collaborative models with an absolute gain of 6% and 21% respectively

Embodied Collaborative Referring Expression Generation in Situated Human-Robot Interaction

ABSTRACT To facilitate referential communication between humans and robots and mediate their differences in representing the shared environment, we are exploring embodied collaborative models for referring expression generation (REG). Instead of a single minimum description to describe a target object, episodes of expressions are generated based on human feedback during human-robot interaction. We particularly investigate the role of embodiment such as robot gesture behaviors (i.e., pointing to an object) and human's gaze feedback (i.e., looking at a particular object) in the collaborative process. This paper examines different strategies of incorporating embodiment and collaboration in REG and discusses their possibilities and challenges in enabling human-robot referential communication